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You hear the term text analytics a lot these days.  It seems to be used in so many different ways it is hard to pin down just what it means.  I’ll try to clear it up a bit.

The basic idea is that a computer looks at some text to accomplish some goal or provide some service.  Just what those goals and services might be we will get to in a moment.  First note that text analytics works with text and analyzes it in some way.  That’s the analytics part.  When I say text, I mean electronic text, like this blog page or some comments from your customers stored in a spreadsheet.  Text analytics does not work with voice recordings, videos, or pictures.

Because text analytics examines text written in a natural language (like English or French), it uses techniques from the sciences of Natural Language Processing (NLP) and Linguistic Analysis.  The computer techniques used include machine learning, word databases (like a dictionary in a database), taxonomies, part of speech tagging, sentence parsing, and so on.  You don’t need to understand these techniques to understand what text analytics does, or how to use it.

Just what can we use text analytics for?  Some of the more common uses are:

• Document classification and indexing – Given a bunch of documents, the computer can figure out the key concepts and let you search a library of documents by these. A sophisticated example of this is E-Discovery used in legal practice, which seeks to use the computer to assist in discovery in legal proceedings.
• National security – Governments use computers to monitor web postings for information or discussions of interest to national security.
• Ad serving – We are all experienced with the uncanny ability of some web sites to show us ads that are relevant to our needs and interests. Text analytics applied to the pages we are viewing is a big part of this magic.
• Business intelligence – For most of us this is the big one! We can the computer to give us insights into how to improve our service, retain customers, and give us a competitive advantage.

Text analytics for business intelligence is a rapidly growing market.  Using the right tool, you can analyze thousands of customer comments in minutes.  If the tool does a good job of presenting the results it is amazing how quickly you can figure out what they care about, their pain points and plaudits, your weaknesses and strengths.

Choosing a Text Analytics Tool

How do you find the right software product for your needs?  Well, there are many providers of raw linguistic analysis capabilities.  Google, Microsoft, Amazon, SAP, IBM, and many others provide such services via an API.  But this takes an engineering effort on your part, and you still need to figure out how to navigate the results of the analysis.

There are several vendors of complete text analytics packages for customer experience management.  As you evaluate these consider:

• Does the vendor specialize in customer experience feedback?
• Are the results of the analysis clear and insightful?
• Is text analytics a core competency or a side product?
• Are multiple languages supported?
• Is automatic translation between languages supported?
• How easy is it to tailor the text analytics to your specific business?

Navigating the Linguistic Analysis Results

Suppose you have a database of feedback from your customers.  If you run 10,000 customer comments through a linguistic analysis engine it will produce a mountain of data.  To gain insight from this data it needs to be organized and navigable.  A good text analytics tool will organize the results and display them in a manner that helps you to find the actionable insights in the comments.  Optimally the tool will help you pinpoint the strengths and weaknesses of your company from the customer's viewpoint.

Reducing the Raw Linguistic Analysis Data

Let's look at a specific example to gain an understanding of what is involved in organizing and presenting the results of linguistic analysis.  As I described in Linguistic Analysis Explained, sentence parsing is one tool in the NLP analytics bag of tricks.  Google has a very well regarded sentence parser, and it is available via an API.  You can try it out at https://cloud.google.com/natural-language/.  It's a fun way to get some insight into the complexities of presenting the results of linguistic analysis.  Try running this comment through the API:

The support staff are helpful, but slow to respond.

Now and take a look at the Syntax results.  These are the results of the sentence parser.  You find:

Wow, that's a lot of data, but hardly actionable insight!  You can see that the sentence has been broken into tokens (words and punctuation).  Each token has been tagged with its part of speech (slow is an adjective).  Each token has also been assigned a parse label, indicating how it is used in the sentence (slow is used as a conjunction).  The green arrows show how the token are interrelated.  Imagine how much data would be generated by running 10,000 customer comments through the sentence parser!

The job of text analytics is to distill this pile of data down.  In this case the analysis might go something like this:

• "Support staff" is the nominal subject of the root verb.  That's a topic the customer has mentioned.
• "Helpful" is an adjectival complement (acomp) of the subject.  The customer has said the staff are helpful.
• "Support staff" is further described by the customer by the coordinating conjunction (cc) "but", as "slow" (conj).

So we find that the support staff are both helpful and slow.  We have extracted a topic of "support staff", with expressions of "helpful" and "slow" associated with it.  This reduction of the raw data from linguistic analysis has resulted in what we are interested in knowing.  Our customer thinks the support staff is helpful, but also slow!  This is a single finding from linguistic analysis.

Presentation of the Reduced Data

Now that we have extracted the desired nuggets from the raw linguistic analysis data we need to present it in a way that helps you find the insights you seek.  An actual analysis of 10,000 customer comments may well produce 50,000 findings.  To navigate these the findings need to be organized in a way that emphasises the important insights, and allows you to explore the findings quickly and intuitively.  A good text analytics tools will assist you in ways such as:

• Grouping similar topics, preferably automatically or with a custom taxonomy you control.
• Ranking topics by frequency of occurrence or other metrics such as sentiment.
• Allowing you to filter the results in various ways, such as by demographic data.
• Showing trends across time or other variables.

Summary

Text analytics for customer experience management reduces and organizes the results of linguistic analysis.  If done well, the results are presented to you, the user, such that you can find actionable insights quickly, and explore the data to help formulate an action plan for improvement.

Editor’s note: This post was originally published on Ascribe in October 2020 and has been updated to reflect the latest data.

Sentiment analysis (or opinion mining) is used to understand the emotion or sentiment behind comments and text, allowing data analysts to gain actionable insights from verbatim comments. While measuring and understanding sentiment analysis scores is more involved than analyzing closed questions, it offers a valuable source of metric data.

What Does Sentiment Mean?

Audiences will have opinions on products, services, and more, that are either positive, negative, or neutral in tone. Companies can use this information to better understand the feedback given by audiences on products or how effective or ineffective messaging has been. Sentiment analysis provides your business with a way to quantify these emotions to discover the overall answer polarity and insights into your customer feedback.

Because customer sentiment is provided in the person’s voice, and not based on a set response or keywords, you need a way for your computers to understand it. Natural Language Processing (NLP), combined with machine learning, allows your sentiment analysis solution to look at a data set and pull more meaning from it. It does this by scoring each response based on whether the algorithm thinks that it’s positive, negative, or neutral.

While applying a sentiment score to the entire response can be useful, it does have problems.  For example, would you say that this comment is positive or negative?

The food was great but the service was awful.

More sophisticated sentiment analysis can apply sentiment scores to sections of a response:

Food-great: positive
Service-awful: negative

Analyzing versus Interpreting

While analysis and interpretation are often used interchangeably, they have two different meanings, especially within data science and sentiment analysis work. Interpreting sentiment in a series of responses is more of a qualitative assessment. If you are manually processing verbatim comments to determine the sentiment, your overall sentiment results could contain unique biases and possible errors. With sentiment analysis tools, this bias potential and possible interpretation errors are severely diminished in favor of a faster, automated, analysis program.

Sentiment analysis programs have a standardized approach that gets the same results regardless of the person running the process. It’s difficult to achieve this manually, but computer-aided methods make it possible.

Positive and Negative Keywords

Positive Words

These text analysis words are representative of positive sentiment. However, despite lists like this existing, these words are subject to change and machine learning models are incredibly sensitive to context. This makes the framing of the comment incredibly important. With these machine learning models, however, companies are able to find out what people like about products, and services while highlighting their experiences. This is a good way to see what you’re doing right and areas where you compare favorably to the competition. You can build on these successes as you move forward as a company.

Here are some of these words:

• Acclaim
• Brilliant
• Convenient
• Durable
• Enjoyable
• Ethical

Negative Words

These words are commonly associated with negative sentiment. These sentiments can indicate areas where you’re failing to deliver on expectations. It’s also a good way to see whether a product or service has a widespread problem during a rollout, to identify issues in the customer experience, and to find other areas of improvement that you can prioritize.

Here are some of these words:

• Dishonest
• Failure
• Gruesome
• Hazardous
• Imbalance
• Lackadaisical

Neutral Sentiment Words

Neutral sentiments are driven by context, so it’s important to look at the whole comment. Excelling in the customer experience means going beyond “okay” and moving in a positive direction. These middle-of-the-road sentiments are useful in determining whether your company is noteworthy in a product or service category.

Positive to Negative Comment Ratio

A ratio in sentiment analysis is a score that looks at how negative sentiment comments and positive sentiment comments are represented. Generally, this is represented on a scale of -1 to 1, with the low end of the scale indicating negative responses and the high end of the scale indicating positive responses. You may need to adjust how you evaluate the score to account for trends in your audience as some may be more negative than the standard population. For example, if you were conducting a survey that focused on dissatisfied customers, then you would be dealing with a tone that’s more negative than usual.

What is a Good Sentiment Score?

A good sentiment score depends on the scoring model that you’re using. Set minimum scores for your positive and negative threshold so you have a scoring system that works best for your use case.

How Accurate is Sentiment Analysis?

The accuracy of sentiment analysis depends on the method that you’re using to work with your verbatim comments, the quality of the data that you’ve given the computer, and the subjectivity of the sentiment. You want the most accurate results possible, which typically means that you’ll want to have a computer assisting your researchers with this process. The automated system can reduce the potential for bias and also use a standardized set of rules for going through the data. If there are any problems with accuracy, you can feed more data into the sentiment analysis solution to help it learn what you’re looking for.

What Algorithm is Best for Sentiment Analysis?

The algorithm that works best for sentiment analysis depends on your resources and your business needs. There are three major categories in algorithms: machine learning, lexicon-based, and those that combine both machine learning algorithms and lexicons.

Machine learning is one of the most popular Algorithms in both Data Science and Text Analytics and is an application of artificial intelligence. It allows your sentiment analysis solution to keep up with changing language in real-time. Because data scientists can’t predict when the next shift in colloquial slang and voice will occur and completely change what is negative and what is positive. They’ve begun to use machine learning with operational data provided to understand natural language and current vernacular. This is a core component of sentiment analysis and is an example of supervised learning, where you’re feeding it representative results so it can learn from them. Unsupervised learning refers to machine learning that is not based on data specifically designed to train it. Deep learning refers to the complexity of machine learning, with this moniker usually referring to complex neural networks.

A lexicon-based algorithm relies on a list of words and phrases and whether they’re positive or negative. It’s difficult to update the lexicon with the latest trends in language.

Ascribe’s dedication to Sentiment Analysis

If you are looking to leverage sentiment analysis when analyzing verbatim comments and open-ended text responses to uncover insights and empower decision-making, check out Ascribe’s text analytics offering, CX Inspector.

CX Inspector is a customizable and interactive text analytics tool with compatible APIs and unique machine learning techniques that provide topic and sentiment analysis from verbatim comments automatically. Analyze everything from the success of marketing campaigns, product feedback results, product reviews, social media platform comments, and more.

For a more comprehensive solution for sentiment analysis, use X-Score which is a feature within CX Inspector that provides a sentiment score from open-ended comments. X-Score is a great measure of customer satisfaction, and also identifies the largest drivers of positive and negative sentiment.

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